Blame - docs/dyn/vision_v1.images.html - platform/external/python/google-api-python-client

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"medical": "A String", # Likelihood that this is a medical image.

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"spoof": "A String", # Spoof likelihood. The likelihood that an modification

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# was made to the image's canonical version to make it appear

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# funny or offensive.

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"violence": "A String", # Violence likelihood.

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"adult": "A String", # Represents the adult content likelihood for the image.

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},

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"textAnnotations": [ # If present, text (OCR) detection has completed successfully.

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{ # Set of detected entity features.

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"confidence": 3.14, # The accuracy of the entity detection in an image.

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# For example, for an image in which the "Eiffel Tower" entity is detected,

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# this field represents the confidence that there is a tower in the query

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# image. Range [0, 1].

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"description": "A String", # Entity textual description, expressed in its `locale` language.

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"locale": "A String", # The language code for the locale in which the entity textual

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# `description` is expressed.

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"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

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# image. For example, the relevancy of "tower" is likely higher to an image

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# containing the detected "Eiffel Tower" than to an image containing a

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# detected distant towering building, even though the confidence that

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# there is a tower in each image may be the same. Range [0, 1].

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"locations": [ # The location information for the detected entity. Multiple

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# `LocationInfo` elements can be present because one location may

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# indicate the location of the scene in the image, and another location

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# may indicate the location of the place where the image was taken.

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# Location information is usually present for landmarks.

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{ # Detected entity location information.

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"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates.

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# of doubles representing degrees latitude and degrees longitude. Unless

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# specified otherwise, this must conform to the

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# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

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# standard</a>. Values must be within normalized ranges.

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#

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# Example of normalization code in Python:

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#

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# def NormalizeLongitude(longitude):

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# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

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# q, r = divmod(longitude, 360.0)

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# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

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# """Wraps decimal degrees latitude and longitude to

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# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

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# r = latitude % 360.0

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# if r <= 90.0:

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# return r, NormalizeLongitude(longitude)

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# elif r >= 270.0:

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# return r - 360, NormalizeLongitude(longitude)

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# else:

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# return 180 - r, NormalizeLongitude(longitude + 180.0)

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#

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# assert 180.0 == NormalizeLongitude(180.0)

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# assert -180.0 == NormalizeLongitude(-180.0)

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# assert -179.0 == NormalizeLongitude(181.0)

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# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

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# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

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# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

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# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

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# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

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# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

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# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

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# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

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# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

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# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

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#

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# The code in logs/storage/validator/logs_validator_traits.cc treats this type

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# as if it were annotated as ST_LOCATION.

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"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

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"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

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},

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},

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],

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"mid": "A String", # Opaque entity ID. Some IDs may be available in

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# [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).

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"score": 3.14, # Overall score of the result. Range [0, 1].

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"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced

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# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

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# are produced for the entire text detected in an image region, followed by

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# `boundingPoly`s for each word within the detected text.

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"vertices": [ # The bounding polygon vertices.

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{ # A vertex represents a 2D point in the image.

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# NOTE: the vertex coordinates are in the same scale as the original image.

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"y": 42, # Y coordinate.

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"x": 42, # X coordinate.

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},

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],

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},

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"properties": [ # Some entities may have optional user-supplied `Property` (name/value)

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# fields, such a score or string that qualifies the entity.

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{ # A `Property` consists of a user-supplied name/value pair.

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"uint64Value": "A String", # Value of numeric properties.

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"name": "A String", # Name of the property.

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"value": "A String", # Value of the property.

},

],

},

],

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"webDetection": { # Relevant information for the image from the Internet. # If present, web detection has completed successfully.

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"pagesWithMatchingImages": [ # Web pages containing the matching images from the Internet.

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{ # Metadata for web pages.

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"url": "A String", # The result web page URL.

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"score": 3.14, # Overall relevancy score for the web page.

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# Not normalized and not comparable across different image queries.

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},

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],

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"webEntities": [ # Deduced entities from similar images on the Internet.

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{ # Entity deduced from similar images on the Internet.

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"entityId": "A String", # Opaque entity ID.

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"score": 3.14, # Overall relevancy score for the entity.

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# Not normalized and not comparable across different image queries.

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"description": "A String", # Canonical description of the entity, in English.

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},

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],

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"partialMatchingImages": [ # Partial matching images from the Internet.

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# Those images are similar enough to share some key-point features. For

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# example an original image will likely have partial matching for its crops.

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{ # Metadata for online images.

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"url": "A String", # The result image URL.

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"score": 3.14, # Overall relevancy score for the image.

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# Not normalized and not comparable across different image queries.

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},

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],

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"fullMatchingImages": [ # Fully matching images from the Internet.

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# They're definite neardups and most often a copy of the query image with

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# merely a size change.

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{ # Metadata for online images.

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"url": "A String", # The result image URL.

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"score": 3.14, # Overall relevancy score for the image.

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# Not normalized and not comparable across different image queries.

},

],

},

"error": { # The `Status` type defines a logical error model that is suitable for different # If set, represents the error message for the operation.

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# Note that filled-in image annotations are guaranteed to be

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# correct, even when `error` is set.

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# programming environments, including REST APIs and RPC APIs. It is used by

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# [gRPC](https://github.com/grpc). The error model is designed to be:

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#

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# - Simple to use and understand for most users

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# - Flexible enough to meet unexpected needs

#

# # Overview

#

# The `Status` message contains three pieces of data: error code, error message,

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# and error details. The error code should be an enum value of

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# google.rpc.Code, but it may accept additional error codes if needed. The

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# error message should be a developer-facing English message that helps

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# developers *understand* and *resolve* the error. If a localized user-facing

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# error message is needed, put the localized message in the error details or

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# localize it in the client. The optional error details may contain arbitrary

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# information about the error. There is a predefined set of error detail types

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# in the package `google.rpc` which can be used for common error conditions.

#

# # Language mapping

#

# The `Status` message is the logical representation of the error model, but it

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# is not necessarily the actual wire format. When the `Status` message is

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# exposed in different client libraries and different wire protocols, it can be

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# mapped differently. For example, it will likely be mapped to some exceptions

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# in Java, but more likely mapped to some error codes in C.

#

# # Other uses

#

# The error model and the `Status` message can be used in a variety of

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# environments, either with or without APIs, to provide a

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# consistent developer experience across different environments.

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#

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# Example uses of this error model include:

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#

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# - Partial errors. If a service needs to return partial errors to the client,

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# it may embed the `Status` in the normal response to indicate the partial

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# errors.

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#

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# - Workflow errors. A typical workflow has multiple steps. Each step may

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# have a `Status` message for error reporting purpose.

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#

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# - Batch operations. If a client uses batch request and batch response, the

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# `Status` message should be used directly inside batch response, one for

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# each error sub-response.

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#

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# - Asynchronous operations. If an API call embeds asynchronous operation

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# results in its response, the status of those operations should be

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# represented directly using the `Status` message.

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#

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# - Logging. If some API errors are stored in logs, the message `Status` could

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# be used directly after any stripping needed for security/privacy reasons.

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"message": "A String", # A developer-facing error message, which should be in English. Any

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# user-facing error message should be localized and sent in the

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# google.rpc.Status.details field, or localized by the client.

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"code": 42, # The status code, which should be an enum value of google.rpc.Code.

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"details": [ # A list of messages that carry the error details. There will be a

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# common set of message types for APIs to use.

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{

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"a_key": "", # Properties of the object. Contains field @type with type URL.

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},

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],

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},

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"labelAnnotations": [ # If present, label detection has completed successfully.

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{ # Set of detected entity features.

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"confidence": 3.14, # The accuracy of the entity detection in an image.

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# For example, for an image in which the "Eiffel Tower" entity is detected,

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# this field represents the confidence that there is a tower in the query

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# image. Range [0, 1].

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"description": "A String", # Entity textual description, expressed in its `locale` language.

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"locale": "A String", # The language code for the locale in which the entity textual

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# `description` is expressed.

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"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

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# image. For example, the relevancy of "tower" is likely higher to an image

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# containing the detected "Eiffel Tower" than to an image containing a

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# detected distant towering building, even though the confidence that

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# there is a tower in each image may be the same. Range [0, 1].

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"locations": [ # The location information for the detected entity. Multiple

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# `LocationInfo` elements can be present because one location may

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# indicate the location of the scene in the image, and another location

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# may indicate the location of the place where the image was taken.

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# Location information is usually present for landmarks.

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{ # Detected entity location information.

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"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates.

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# of doubles representing degrees latitude and degrees longitude. Unless

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# specified otherwise, this must conform to the

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# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

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# standard</a>. Values must be within normalized ranges.

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#

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# Example of normalization code in Python:

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#

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# def NormalizeLongitude(longitude):

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# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

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# q, r = divmod(longitude, 360.0)

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# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

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# """Wraps decimal degrees latitude and longitude to

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# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

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# r = latitude % 360.0

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# if r <= 90.0:

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# return r, NormalizeLongitude(longitude)

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# elif r >= 270.0:

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# return r - 360, NormalizeLongitude(longitude)

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# else:

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# return 180 - r, NormalizeLongitude(longitude + 180.0)

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#

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# assert 180.0 == NormalizeLongitude(180.0)

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# assert -180.0 == NormalizeLongitude(-180.0)

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# assert -179.0 == NormalizeLongitude(181.0)

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# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

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# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

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# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

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# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

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# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

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# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

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# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

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# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

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# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

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# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

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#

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# The code in logs/storage/validator/logs_validator_traits.cc treats this type

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# as if it were annotated as ST_LOCATION.

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"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

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"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

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},

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},

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],

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"mid": "A String", # Opaque entity ID. Some IDs may be available in

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# [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).

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"score": 3.14, # Overall score of the result. Range [0, 1].

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"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced

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# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

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# are produced for the entire text detected in an image region, followed by

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# `boundingPoly`s for each word within the detected text.

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"vertices": [ # The bounding polygon vertices.

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{ # A vertex represents a 2D point in the image.

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# NOTE: the vertex coordinates are in the same scale as the original image.

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"y": 42, # Y coordinate.

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"x": 42, # X coordinate.

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},

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],

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},

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"properties": [ # Some entities may have optional user-supplied `Property` (name/value)

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# fields, such a score or string that qualifies the entity.

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{ # A `Property` consists of a user-supplied name/value pair.

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"uint64Value": "A String", # Value of numeric properties.

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"name": "A String", # Name of the property.

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"value": "A String", # Value of the property.

},

],

},

],

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"imagePropertiesAnnotation": { # Stores image properties, such as dominant colors. # If present, image properties were extracted successfully.

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"dominantColors": { # Set of dominant colors and their corresponding scores. # If present, dominant colors completed successfully.

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"colors": [ # RGB color values with their score and pixel fraction.

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{ # Color information consists of RGB channels, score, and the fraction of

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# the image that the color occupies in the image.

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"color": { # Represents a color in the RGBA color space. This representation is designed # RGB components of the color.

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# for simplicity of conversion to/from color representations in various

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# languages over compactness; for example, the fields of this representation

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# can be trivially provided to the constructor of "java.awt.Color" in Java; it

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# can also be trivially provided to UIColor's "+colorWithRed:green:blue:alpha"

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# method in iOS; and, with just a little work, it can be easily formatted into

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# a CSS "rgba()" string in JavaScript, as well. Here are some examples:

#

# Example (Java):

#

# import com.google.type.Color;

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#

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# // ...

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# public static java.awt.Color fromProto(Color protocolor) {

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# float alpha = protocolor.hasAlpha()

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# ? protocolor.getAlpha().getValue()

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# : 1.0;

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#

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# return new java.awt.Color(

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# protocolor.getRed(),

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# protocolor.getGreen(),

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# protocolor.getBlue(),

# alpha);

# }

#

# public static Color toProto(java.awt.Color color) {

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# float red = (float) color.getRed();

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# float green = (float) color.getGreen();

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# float blue = (float) color.getBlue();

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# float denominator = 255.0;

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# Color.Builder resultBuilder =

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# Color

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# .newBuilder()

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# .setRed(red / denominator)

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# .setGreen(green / denominator)

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# .setBlue(blue / denominator);

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# int alpha = color.getAlpha();

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# if (alpha != 255) {

# result.setAlpha(

# FloatValue

# .newBuilder()

# .setValue(((float) alpha) / denominator)

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# .build());

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# }

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# return resultBuilder.build();

# }

# // ...

#

# Example (iOS / Obj-C):

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#

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# // ...

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# static UIColor* fromProto(Color* protocolor) {

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# float red = [protocolor red];

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# float green = [protocolor green];

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# float blue = [protocolor blue];

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# FloatValue* alpha_wrapper = [protocolor alpha];

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# float alpha = 1.0;

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# if (alpha_wrapper != nil) {

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# alpha = [alpha_wrapper value];

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# }

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# return [UIColor colorWithRed:red green:green blue:blue alpha:alpha];

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# }

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#

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# static Color* toProto(UIColor* color) {

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# CGFloat red, green, blue, alpha;

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# if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) {

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# return nil;

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# }

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# Color* result = [Color alloc] init];

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# [result setRed:red];

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# [result setGreen:green];

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# [result setBlue:blue];

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# if (alpha <= 0.9999) {

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# [result setAlpha:floatWrapperWithValue(alpha)];

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# }

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# [result autorelease];

# return result;

# }

# // ...

#

# Example (JavaScript):

#

# // ...

#

# var protoToCssColor = function(rgb_color) {

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# var redFrac = rgb_color.red || 0.0;

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# var greenFrac = rgb_color.green || 0.0;

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# var blueFrac = rgb_color.blue || 0.0;

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# var red = Math.floor(redFrac * 255);

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# var green = Math.floor(greenFrac * 255);

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# var blue = Math.floor(blueFrac * 255);

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#

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# if (!('alpha' in rgb_color)) {

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# return rgbToCssColor_(red, green, blue);

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# }

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#

651

# var alphaFrac = rgb_color.alpha.value || 0.0;

652

# var rgbParams = [red, green, blue].join(',');

653

# return ['rgba(', rgbParams, ',', alphaFrac, ')'].join('');

654

# };

655

#

656

# var rgbToCssColor_ = function(red, green, blue) {

657

# var rgbNumber = new Number((red << 16) | (green << 8) | blue);

658

# var hexString = rgbNumber.toString(16);

659

# var missingZeros = 6 - hexString.length;

660

# var resultBuilder = ['#'];

661

# for (var i = 0; i < missingZeros; i++) {

662

# resultBuilder.push('0');

663

# }

664

# resultBuilder.push(hexString);

665

# return resultBuilder.join('');

# };

#

# // ...

"blue": 3.14, # The amount of blue in the color as a value in the interval [0, 1].

670

"alpha": 3.14, # The fraction of this color that should be applied to the pixel. That is,

671

# the final pixel color is defined by the equation:

672

#

673

# pixel color = alpha * (this color) + (1.0 - alpha) * (background color)

674

#

675

# This means that a value of 1.0 corresponds to a solid color, whereas

676

# a value of 0.0 corresponds to a completely transparent color. This

677

# uses a wrapper message rather than a simple float scalar so that it is

678

# possible to distinguish between a default value and the value being unset.

679

# If omitted, this color object is to be rendered as a solid color

680

# (as if the alpha value had been explicitly given with a value of 1.0).

681

"green": 3.14, # The amount of green in the color as a value in the interval [0, 1].

682

"red": 3.14, # The amount of red in the color as a value in the interval [0, 1].

683

},

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

684

"pixelFraction": 3.14, # The fraction of pixels the color occupies in the image.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

685

# Value in range [0, 1].

686

"score": 3.14, # Image-specific score for this color. Value in range [0, 1].

},

],

},

},

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

691

"faceAnnotations": [ # If present, face detection has completed successfully.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

692

{ # A face annotation object contains the results of face detection.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

693

"panAngle": 3.14, # Yaw angle, which indicates the leftward/rightward angle that the face is

694

# pointing relative to the vertical plane perpendicular to the image. Range

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

695

# [-180,180].

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

696

"underExposedLikelihood": "A String", # Under-exposed likelihood.

697

"detectionConfidence": 3.14, # Detection confidence. Range [0, 1].

698

"joyLikelihood": "A String", # Joy likelihood.

699

"landmarks": [ # Detected face landmarks.

700

{ # A face-specific landmark (for example, a face feature).

701

# Landmark positions may fall outside the bounds of the image

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

702

# if the face is near one or more edges of the image.

703

# Therefore it is NOT guaranteed that `0 <= x < width` or

704

# `0 <= y < height`.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

705

"position": { # A 3D position in the image, used primarily for Face detection landmarks. # Face landmark position.

706

# A valid Position must have both x and y coordinates.

707

# The position coordinates are in the same scale as the original image.

708

"y": 3.14, # Y coordinate.

709

"x": 3.14, # X coordinate.

710

"z": 3.14, # Z coordinate (or depth).

711

},

712

"type": "A String", # Face landmark type.

713

},

714

],

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

715

"sorrowLikelihood": "A String", # Sorrow likelihood.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

716

"blurredLikelihood": "A String", # Blurred likelihood.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

717

"tiltAngle": 3.14, # Pitch angle, which indicates the upwards/downwards angle that the face is

718

# pointing relative to the image's horizontal plane. Range [-180,180].

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

719

"angerLikelihood": "A String", # Anger likelihood.

720

"boundingPoly": { # A bounding polygon for the detected image annotation. # The bounding polygon around the face. The coordinates of the bounding box

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

721

# are in the original image's scale, as returned in `ImageParams`.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

722

# The bounding box is computed to "frame" the face in accordance with human

723

# expectations. It is based on the landmarker results.

724

# Note that one or more x and/or y coordinates may not be generated in the

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

725

# `BoundingPoly` (the polygon will be unbounded) if only a partial face

726

# appears in the image to be annotated.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

727

"vertices": [ # The bounding polygon vertices.

728

{ # A vertex represents a 2D point in the image.

729

# NOTE: the vertex coordinates are in the same scale as the original image.

730

"y": 42, # Y coordinate.

731

"x": 42, # X coordinate.

732

},

733

],

734

},

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

735

"rollAngle": 3.14, # Roll angle, which indicates the amount of clockwise/anti-clockwise rotation

736

# of the face relative to the image vertical about the axis perpendicular to

737

# the face. Range [-180,180].

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

738

"headwearLikelihood": "A String", # Headwear likelihood.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

739

"surpriseLikelihood": "A String", # Surprise likelihood.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

740

"fdBoundingPoly": { # A bounding polygon for the detected image annotation. # The `fd_bounding_poly` bounding polygon is tighter than the

741

# `boundingPoly`, and encloses only the skin part of the face. Typically, it

742

# is used to eliminate the face from any image analysis that detects the

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

743

# "amount of skin" visible in an image. It is not based on the

744

# landmarker results, only on the initial face detection, hence

745

# the <code>fd</code> (face detection) prefix.

746

"vertices": [ # The bounding polygon vertices.

747

{ # A vertex represents a 2D point in the image.

748

# NOTE: the vertex coordinates are in the same scale as the original image.

749

"y": 42, # Y coordinate.

750

"x": 42, # X coordinate.

},

],

},

"landmarkingConfidence": 3.14, # Face landmarking confidence. Range [0, 1].

755

},

756

],

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

757

"logoAnnotations": [ # If present, logo detection has completed successfully.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

758

{ # Set of detected entity features.

759

"confidence": 3.14, # The accuracy of the entity detection in an image.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

760

# For example, for an image in which the "Eiffel Tower" entity is detected,

761

# this field represents the confidence that there is a tower in the query

762

# image. Range [0, 1].

763

"description": "A String", # Entity textual description, expressed in its `locale` language.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

764

"locale": "A String", # The language code for the locale in which the entity textual

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

765

# `description` is expressed.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

766

"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

767

# image. For example, the relevancy of "tower" is likely higher to an image

768

# containing the detected "Eiffel Tower" than to an image containing a

769

# detected distant towering building, even though the confidence that

770

# there is a tower in each image may be the same. Range [0, 1].

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

771

"locations": [ # The location information for the detected entity. Multiple

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

772

# `LocationInfo` elements can be present because one location may

773

# indicate the location of the scene in the image, and another location

774

# may indicate the location of the place where the image was taken.

775

# Location information is usually present for landmarks.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

776

{ # Detected entity location information.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

777

"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

778

# of doubles representing degrees latitude and degrees longitude. Unless

779

# specified otherwise, this must conform to the

780

# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

781

# standard</a>. Values must be within normalized ranges.

782

#

783

# Example of normalization code in Python:

784

#

785

# def NormalizeLongitude(longitude):

786

# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

787

# q, r = divmod(longitude, 360.0)

788

# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

793

# """Wraps decimal degrees latitude and longitude to

794

# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

795

# r = latitude % 360.0

796

# if r <= 90.0:

797

# return r, NormalizeLongitude(longitude)

798

# elif r >= 270.0:

799

# return r - 360, NormalizeLongitude(longitude)

800

# else:

801

# return 180 - r, NormalizeLongitude(longitude + 180.0)

802

#

803

# assert 180.0 == NormalizeLongitude(180.0)

804

# assert -180.0 == NormalizeLongitude(-180.0)

805

# assert -179.0 == NormalizeLongitude(181.0)

806

# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

807

# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

808

# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

809

# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

810

# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

811

# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

812

# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

813

# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

814

# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

815

# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

816

#

817

# The code in logs/storage/validator/logs_validator_traits.cc treats this type

818

# as if it were annotated as ST_LOCATION.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

819

"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

820

"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

821

},

822

},

823

],

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

824

"mid": "A String", # Opaque entity ID. Some IDs may be available in

825

# [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

826

"score": 3.14, # Overall score of the result. Range [0, 1].

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

827

"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

828

# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

829

# are produced for the entire text detected in an image region, followed by

830

# `boundingPoly`s for each word within the detected text.

831

"vertices": [ # The bounding polygon vertices.

832

{ # A vertex represents a 2D point in the image.

833

# NOTE: the vertex coordinates are in the same scale as the original image.

834

"y": 42, # Y coordinate.

835

"x": 42, # X coordinate.

836

},

837

],

838

},

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

839

"properties": [ # Some entities may have optional user-supplied `Property` (name/value)

840

# fields, such a score or string that qualifies the entity.

841

{ # A `Property` consists of a user-supplied name/value pair.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

842

"uint64Value": "A String", # Value of numeric properties.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

843

"name": "A String", # Name of the property.

844

"value": "A String", # Value of the property.

},

],

},

],

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

849

"landmarkAnnotations": [ # If present, landmark detection has completed successfully.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

850

{ # Set of detected entity features.

851

"confidence": 3.14, # The accuracy of the entity detection in an image.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

852

# For example, for an image in which the "Eiffel Tower" entity is detected,

853

# this field represents the confidence that there is a tower in the query

854

# image. Range [0, 1].

855

"description": "A String", # Entity textual description, expressed in its `locale` language.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

856

"locale": "A String", # The language code for the locale in which the entity textual

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

857

# `description` is expressed.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

858

"topicality": 3.14, # The relevancy of the ICA (Image Content Annotation) label to the

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

859

# image. For example, the relevancy of "tower" is likely higher to an image

860

# containing the detected "Eiffel Tower" than to an image containing a

861

# detected distant towering building, even though the confidence that

862

# there is a tower in each image may be the same. Range [0, 1].

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

863

"locations": [ # The location information for the detected entity. Multiple

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

864

# `LocationInfo` elements can be present because one location may

865

# indicate the location of the scene in the image, and another location

866

# may indicate the location of the place where the image was taken.

867

# Location information is usually present for landmarks.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

868

{ # Detected entity location information.

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

869

"latLng": { # An object representing a latitude/longitude pair. This is expressed as a pair # lat/long location coordinates.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

870

# of doubles representing degrees latitude and degrees longitude. Unless

871

# specified otherwise, this must conform to the

872

# <a href="http://www.unoosa.org/pdf/icg/2012/template/WGS_84.pdf">WGS84

873

# standard</a>. Values must be within normalized ranges.

874

#

875

# Example of normalization code in Python:

876

#

877

# def NormalizeLongitude(longitude):

878

# """Wraps decimal degrees longitude to [-180.0, 180.0]."""

879

# q, r = divmod(longitude, 360.0)

880

# if r > 180.0 or (r == 180.0 and q <= -1.0):

# return r - 360.0

# return r

#

# def NormalizeLatLng(latitude, longitude):

885

# """Wraps decimal degrees latitude and longitude to

886

# [-90.0, 90.0] and [-180.0, 180.0], respectively."""

887

# r = latitude % 360.0

888

# if r <= 90.0:

889

# return r, NormalizeLongitude(longitude)

890

# elif r >= 270.0:

891

# return r - 360, NormalizeLongitude(longitude)

892

# else:

893

# return 180 - r, NormalizeLongitude(longitude + 180.0)

894

#

895

# assert 180.0 == NormalizeLongitude(180.0)

896

# assert -180.0 == NormalizeLongitude(-180.0)

897

# assert -179.0 == NormalizeLongitude(181.0)

898

# assert (0.0, 0.0) == NormalizeLatLng(360.0, 0.0)

899

# assert (0.0, 0.0) == NormalizeLatLng(-360.0, 0.0)

900

# assert (85.0, 180.0) == NormalizeLatLng(95.0, 0.0)

901

# assert (-85.0, -170.0) == NormalizeLatLng(-95.0, 10.0)

902

# assert (90.0, 10.0) == NormalizeLatLng(90.0, 10.0)

903

# assert (-90.0, -10.0) == NormalizeLatLng(-90.0, -10.0)

904

# assert (0.0, -170.0) == NormalizeLatLng(-180.0, 10.0)

905

# assert (0.0, -170.0) == NormalizeLatLng(180.0, 10.0)

906

# assert (-90.0, 10.0) == NormalizeLatLng(270.0, 10.0)

907

# assert (90.0, 10.0) == NormalizeLatLng(-270.0, 10.0)

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

908

#

909

# The code in logs/storage/validator/logs_validator_traits.cc treats this type

910

# as if it were annotated as ST_LOCATION.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

911

"latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].

912

"longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].

913

},

914

},

915

],

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

916

"mid": "A String", # Opaque entity ID. Some IDs may be available in

917

# [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

918

"score": 3.14, # Overall score of the result. Range [0, 1].

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

919

"boundingPoly": { # A bounding polygon for the detected image annotation. # Image region to which this entity belongs. Currently not produced

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

920

# for `LABEL_DETECTION` features. For `TEXT_DETECTION` (OCR), `boundingPoly`s

921

# are produced for the entire text detected in an image region, followed by

922

# `boundingPoly`s for each word within the detected text.

923

"vertices": [ # The bounding polygon vertices.

924

{ # A vertex represents a 2D point in the image.

925

# NOTE: the vertex coordinates are in the same scale as the original image.

926

"y": 42, # Y coordinate.

927

"x": 42, # X coordinate.

928

},

929

],

930

},

Jon Wayne Parrott

2017-01-06 09:58:29 -0800

[diff] [blame]

931

"properties": [ # Some entities may have optional user-supplied `Property` (name/value)

932

# fields, such a score or string that qualifies the entity.

933

{ # A `Property` consists of a user-supplied name/value pair.

Sai Cheemalapati

2017-03-13 12:12:03 -0400

[diff] [blame^]

934

"uint64Value": "A String", # Value of numeric properties.

Jon Wayne Parrott

2016-05-19 10:54:38 -0700

[diff] [blame]

935

"name": "A String", # Name of the property.

936

"value": "A String", # Value of the property.

},

],

},

],

Sai Cheemalapati